As the field of data science explodes, data professionals are increasingly using programming language Python to get work done, over other tools such as R and SQL, according to Harnham's US Data & Analytics Salary Guide 2019, released this week. With more professionals entering the field, the data science industry is moving from traditional "core" data scientists toward those with more specialized skillsets, the report noted. And in an active market, candidates are often able to be selective about the jobs they take. Data scientists move between roles more quickly than professionals in any other part of the tech industry, averaging less than two years in each position, the report found. SEE: Python is eating the world: How one developer's side project became the hottest programming language on the planet (cover story PDF) (TechRepublic) When it comes to tools, a common debate in the data science realm is whether or not Python or R is a better programming language for data work.
It is, however, a thoughtful introduction to and overview of machine-learning methods, appropriately remembering about the context and life-cycle of an ML project, and keeping things hands-on with small Python examples, but managing not to fall into the catalogue mode. I have seen other books try this before. "Doing Data Science" by O'Neill and Schutt comes to mind first, long on enthusiasm but a little short on quality. Then there is Manning's own "Practical Data Science with R" by Zumel and Mount. Among the three, RWML looks like a clear winner. If I had to pick on something, I would register disappointment with the book's one extended exercise, based on the NYC taxi dataset.
Matloff delivers a well-balanced book for advanced beginners. Besides the mathematical formulas, he also presents many chunks of R code, and if the reader is able to read R code, the formulas and calculations become clearer. Due to the computational R code, the well-written Appendix, and an overall clear English, the book will help students and autodidacts. Matloff has written a textbook of the best kind for such a broad topic." ". . . the book is well suitable for a wide audience: For practitioners interested in applying the methodology, for students in statistics as well as economics/social sciences and computer science.
Python and R are among the most frequently mentioned skills in job postings for data science positions. But reports on which programming language is actually used most often on the job for these professionals are conflicting, according to a Thursday report from Cloud Academy. The TIOBE Programming Community Index shows R as being on a downward trend this year in terms of search engine requests. However, a Kaggle survey of 16,000 data professionals found that while Python was the most popular programming language overall, statisticians and data scientists were more likely to report using R at work than other roles. Among data scientists, 87% reported using Python and 71% reported using R at work, that report found.